# RetCompletion:High-Speed Inference Image Completion with Retentive Network Source: [RetCompletion:High-Speed Inference Image Completion with Retentive Network]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:44:34.944Z Authors: Yueyang Cang, Pingge Hu, Xiaoteng Zhang, Xingtong Wang, Yuhang Liu, Li Shi Published: 2024-10-05T06:33:42.000Z ## Abstract Time cost is a major challenge in achieving high-quality pluralistic image completion. Recently, the Retentive Network (RetNet) in natural language processing offers a novel approach to this problem with its low-cost inference capabilities. Inspired by this, we apply RetNet to the pluralistic image completion task in computer vision. We present RetCompletion, a two-stage framework. In the first stage, we introduce Bi-RetNet, a bidirectional sequence information fusion model that integrates contextual information from images. During inference, we employ a unidirectional pixel-wise update strategy to restore consistent image structures, achieving both high reconstruction quality and fast inference speed. In the second stage, we use a CNN for low-resolution upsampling to enhance texture details. Experiments on ImageNet and CelebA-HQ demonstrate that our inference speed is 10$\\times$ faster than ICT and 15$\\times$ faster than RePaint. The proposed RetCompletion significantly improves inference speed and delivers strong performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()